Collision-based probabilistic obstacle avoidance algorithm for swarm robots navigation in unknown environment
Kosuke Sakamoto, Yutaro Koeba, Yasuharu Kunii · Advanced Robotics · 2025
This paper presents a collision-based probabilistic Vector Field Histogram (p-VFH) obstacle avoidance algorithm for swarm robot navigation in unknown environments. Conventional obstacle avoidance strategies, including well-known path planning methods like A* and RRT*, are ill-suited for unknown environments. Moreover, current collision avoidance approaches for robot swarms face challenges related to computational demands, sensor performance, and potential local minima issues, such as deadlocks. Our proposed p-VFH algorithm tackles these problems by employing a probabilistic approach to determine the robot's movement trajectory. This method relies on a dynamically updated polar histogram that represents obstacle density in the surrounding area, and target direction. The algorithm begins by initializing histogram values, which are then updated as the robot encounters unrecorded obstacles. Subsequently, it creates a probability distribution to guide the selection of the next movement direction. To assess the effectiveness of p-VFH, we conducted comprehensive simulation studies. These experiments compared p-VFH's performance against three alternative methods including conventional VFH, a combined probability distribution approach, and a constant weighting function. The results show that p-VFH significantly improves exploration efficiency, successfully guiding robots to designated targets while effectively avoiding obstacles. In particular, p-VFH outperformed the other tested methods in terms of success rates and environmental adaptability. Furthermore, we conducted real-world experiments using a swarm robots equipped with the p-VFH algorithm. These real-world tests confirmed the effectiveness of p-VFH in real-time obstacle avoidance and exploration in unknown environments. The promising results suggest that the p-VFH algorithm could play a crucial role in advancing swarm robotics technology, with potential applications ranging from planetary exploration to various other fields.